Quantitative methods for obtaining tissue characteristics from optical coherence tomography images

ABSTRACT

A method and apparatus for determining properties of a tissue or tissues imaged by optical coherence tomography (OCT). In one embodiment the backscatter and attenuation of the OCT optical beam is measured and based on these measurements and indicium such as color is assigned for each portion of the image corresponding to the specific value of the backscatter and attenuation for that portion. The image is then displayed with the indicia and a user can then determine the tissue characteristics. In an alternative embodiment the tissue characteristics is classified automatically by a program given the combination of backscatter and attenuation values.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application61/058,077 filed on Jun. 2, 2008, the disclosure of which is hereinincorporated by reference in its entirety.

FIELD OF INVENTION

This invention provides methods for tissue characterization usingoptical coherence tomography. Specifically, in part, suchcharacterization can be performed by measuring a tissue's optical andimage properties.

BACKGROUND

Optical coherence tomography (OCT) is an interferometric imagingtechnique with widespread applications in ophthalmology, cardiology,gastroenterology and other fields of medicine. The ability to viewsubsurface structures with high resolution (2-15 μm) throughsmall-diameter fiber-optic probes makes OCT especially useful forminimally invasive imaging of internal tissues and organs. OCT systemscan generate images up to 100 frames per second, making it possible toimage coronary arteries in the beating heart artery within a fewseconds. OCT can be implemented in both time domain (TD-OCT) andfrequency domain (Fourier domain OCT or optical frequency domainimaging, OFDI).

OCT imaging of portions of a patient's body provides a useful tool fordoctors to determine the best type and course of treatment. For example,imaging of coronary arteries by intravascular OCT may reveal thelocation of a stenosis, the presence of vulnerable plaques, or the typeof atherosclerotic plaque. This information helps cardiologists choosewhich treatment would best serve the patient—drug therapy (e.g.,cholesterol-lowering medication), a catheter-based therapy likeangioplasty and stenting, or an invasive surgical procedure likecoronary bypass surgery. In addition to its applications in clinicalmedicine, OCT is also very useful for drug development in animal andclinical trials.

Normal arteries have a consistent layered structure consisting ofintima, media and adventia. As a result of the process ofatherosclerosis, the intima becomes pathologically thickened and maycontain plaques composed of different types of tissues, including fiber,proteoglycans, lipid and calcium, as well as macrophages and otherinflammatory cells. These tissue types have different optical propertiesthat can be measured by OCT. The plaques that are believed to be mostpathologically significant are the so-called vulnerable plaques thathave a fibrous cap with an underlying lipid pool.

In a typical OCT imaging system, an optical probe mounted on a catheteris carefully maneuvered to a point of interest such as within a coronaryblood vessel. The optical beams are then transmitted and thebackscattered signals are received through coherent detection using aninterferometer. As the probe is scanned through a predetermined line orarea, many data lines can be collected. An image (2D or 3D) is thenreconstructed using well-known techniques. This image is then analyzedvisually by a cardiologist to assess pathological features, such asvessel wall thickening and plaque composition.

Since tissue type is identified by its appearance on the screen, errorsmay occur in the analysis because certain information (such as tissuetype) cannot be readily discerned. The standard OCT image only containsthe intensity information of the OCT signals. Small changes in theoptical properties that influence the OCT signals cannot be readilydiscerned. Thus, it would be advantageous to have an OCT system andmethod to measure the optical properties and use them to aid scientistsand clinicians. The present invention addresses this need.

SUMMARY OF THE INVENTION

The methods are explained through the following description, drawings,and claims.

In general the invention relates to a method and apparatus fordetermining properties of a tissue or tissues imaged by OCT. In oneembodiment the backscatter and attenuation of the OCT optical beam ismeasured and based on these measurements an indicium, such as color, isassigned for each portion of the image corresponding to the specificvalue of the backscatter and attenuation for that portion. The image isthen displayed with the indicia and a user can then determine the tissuecharacteristics. Alternatively, the tissue characteristics can beclassified automatically by a program given the combination ofbackscatter and attenuation values.

In one aspect the invention relates to a method for identifying tissuecomponents in situ. In one embodiment the method comprises the steps of:taking an OCT image of a tissue in situ; measuring the attenuation andbackscatter at a point in the OCT image; and determining the compositionof the tissue at a location in the tissue corresponding to the point inthe OCT image in response to the measured attenuation and backscatter.In another embodiment the method further comprises mapping a pair ofcoordinates in backscatter-attenuation space to an indicium of the valueof the pair of coordinates in the backscatter-attenuation space. In oneembodiment the indicium is a color. In another embodiment the methodfurther comprises displaying the indicium corresponding to the measuredattenuation and backscatter at the point in the OCT image.

In another aspect the invention relates to a system for identifyingtissue components in situ. In one embodiment the system comprises an OCTsubsystem for taking an OCT image of a tissue in situ; a processor incommunication with the OCT subsystem for measuring the attenuation andbackscatter at a point in the OCT image and determining the compositionof the tissue at a location in the tissue corresponding to the point inthe OCT image in response to the measured attenuation and backscatter;and a display for displaying the OCT image and an indicium correspondingto the measured attenuation and backscatter at the point in the OCTimage.

In another aspect the invention relates to a processor-implementedmethod for identifying tissue components in situ. In one embodiment, themethod includes the steps of (a) collecting an OCT dataset of a tissuesample in situ using a probe; (b) measuring an attenuation value and abackscattering value at a point in the tissue sample; and (c)determining a tissue characteristic at a location in the tissue samplecorresponding to an image location in an OCT image formed from the OCTdataset in response to the measured attenuation value and backscatteringvalue. The method can include the further step of mapping a pair ofcoordinates in backscatter-attenuation space to an indicium of the valueof the pair of coordinates in the backscatter-attenuation space. Themethod can include the further step of displaying the indiciumcorresponding to the measured attenuation and backscatter at the pointin the OCT image. The tissue characteristic can be selected from thegroup consisting of cholesterol, fiber, fibrous, lipid pool, lipid,fibrofatty, calcium nodule, calcium plate, calcium speckled, thrombus,foam cells, and proteoglycans. The indicium can be, for example, acolor. The indicium can also be selected from the group consisting of anover-lay, a colormap, a texture map, and text. The method can includethe further step of classifying tissue type using a property selectedfrom the group consisting of backscattering, attenuation, edgesharpness, and texture measurements. The method can include the furtherstep of correcting a focusing effect to improve tissue typeclassification. The method can include the further step of applyingangular intensity correction to account for an attenuation effect, suchas, for example, a blood-related attenuation effect. The method caninclude the further step of determining a tissue characteristic using atechnique selected from the group consisting of boundary detection,lumen location, and OCT location depth.

In another aspect the invention relates to a system for identifyingtissue components in situ. In one embodiment, the system includes (a) anOCT subsystem for taking an OCT image of a tissue in situ; (b) aprocessor in communication with the OCT subsystem for measuring theattenuation and backscatter at a point in the OCT image and determininga tissue characteristic of the tissue at a location in the tissuecorresponding to the point in the OCT image in response to the measuredattenuation and backscatter; and (c) a display for displaying the OCTimage and an indicium corresponding to the measured attenuation andbackscatter at the point in the OCT image. The tissue characteristic canbe selected from the group consisting of cholesterol, fiber, fibrous,lipid pool, lipid, fibrofatty, calcium nodule, calcium plate, calciumspeckled, thrombus, foam cells, and proteoglycans.

In another aspect the invention relates to an optical coherencetomography system for identifying tissue characteristics of a sample. Inone embodiment the computer system includes a detector configured toreceive an optical interference signal generated from scanning a sampleand converting the optical interference signal to an electrical signal;an electronic memory device and an electronic processor in communicationwith the memory device and the detector. The memory device can includeinstructions that, when executed by the processor, cause the processorto: analyze the electrical signal and generate a plurality of datasetscorresponding to the sample, wherein one of the plurality of datasetscomprises backscattering data; compare the backscattering data to afirst threshold, the backscattering data mapping to a first location inthe sample; and if the backscattering data exceeds the first threshold,characterize the first location in the sample as having a first tissuecharacteristic. In some embodiments, the processor is further caused togenerate an OCT image of the sample such that the first tissuecharacteristic is identified and displayed relative to the firstlocation. The first tissue characteristic can be selected from the groupconsisting of cholesterol, fiber, fibrous, lipid pool, lipid,fibrofatty, calcium nodule, calcium plate, calcium speckled, thrombus,foam cells, and proteoglycan. In some embodiments, at least one of theplurality of datasets includes OCT scan data, attenuation data, edgesharpness data, texture parameters, or interferometric data.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the Office upon request and paymentof the necessary fee.

The objects and features of the invention can be better understood withreference to the drawings described below, and the claims. The drawingsare not necessarily to scale, emphasis instead generally being placedupon illustrating the principles of the invention. In the drawings,numerals are used to indicate specific parts throughout the variousviews. The drawings associated with the disclosure are addressed on anindividual basis within the disclosure as they are introduced.

FIG. 1 is a schematic view of a generalized OCT data acquisition systemin accordance with an embodiment of the invention.

FIG. 2 is a schematic view of a cross-section of a lumen with an imagingprobe disposed therein with a semi-transparent layer according to anembodiment of the invention.

FIG. 3A shows optical properties, geometric information and parametersfor a beam of electromagnetic waves used to collect OCT data.

FIGS. 3B and 3C show plots of relative intensity and gain, respectively,as a function of beam distance from the probe tip used to perform OCTdata collection according to an embodiment of the invention.

FIG. 4A shows a cross-section of a lumen with an imaging probe disposedtherein according to an embodiment of the invention.

FIG. 4B shows an exemplary angular intensity profile with respect torotational angle of the probe according to an embodiment of theinvention.

FIGS. 5A-5B illustrates the results from the application of an exemplarymethod of OCT image denoising according to an illustrative embodiment ofthe invention. FIG. 5A shows the image before denoising and FIG. 5Bshows the same OCT image after the application of the denoising method.

FIG. 6A illustrates OCT scan data being processed using a window as partof a method for optical property extraction according to an embodimentof the invention.

FIG. 6B illustrates OCT scan data being processed to define a region ofinterest according to an embodiment of the invention.

FIG. 6C is data plot with a linear portion used to model certain opticalproperties from a set of OCT data according to an embodiment of theinvention.

FIG. 7 illustrates a lumen cross-section and demonstrates the automaticdetection of a lumen surface and an OCT penetration limit of interestaccording to an embodiment of the invention.

FIG. 8 illustrates a plotted OCT dataset suitable for performing tissueboundary localization using piece-wise regression on a one-dimensionalOCT axial scan according to an embodiment of the invention.

FIG. 9 illustrates a two-dimensional cross-section of a lumen obtainedusing an OCT scan such that the tissue boundary localization wasgenerated using Canny's edge detector method.

FIG. 10A is a diagram of a section of lumen wall showing the interactionof the beam at two locations with the various tissues in the wall.

FIGS. 10B and 10C are schematic diagrams that illustrate the dimensionsand variables used in a method of extracting attenuation/backscatteringcoefficients from a multi-layered tissue object shown in FIG. 10Aaccording to an embodiment of the invention.

FIG. 11A includes four image data plots showing exemplary tissuecharacterization coefficients relating to backscattering and attenuationdata according to an embodiment of the invention.

FIG. 11B shows a color-map plot of attenuation and backscattering datasuitable for implementing a method for distinguishing different tissueproperties according to an embodiment of the invention.

FIG. 11C shows two exemplary OCT images depicting different tissueproperties that have been enhanced using the color-map shown in FIG.11B.

FIGS. 11D-11E show hatched versions of FIGS. 11B and 11C.

FIGS. 12A and 12B illustrate, respectively, methods of tissuecharacterization by histological preparation and OCT data processing inaccordance with embodiments of present invention.

FIG. 13A shows an example histology image with mapped tissue typesaccording to an embodiment of the invention.

FIG. 13B shows an OCT image in which the tissue types and dataidentified in FIG. 13A have been mapped and identified with boundariesaccording to an embodiment of the invention.

FIG. 14 shows a plot of attenuation data versus backscattering data withrespect to certain tissue properties obtained from real humanatherosclerosis plaques using methods described in FIGS. 12A-12B.

FIG. 15A shows a plot of attenuation data versus backscattering datasuitable for use with a tissue characterization discriminant method thatcompares the tissue properties of region of interest to the tissueproperties populated in a database.

FIG. 15B illustrates an exemplary OCT tissue characterization image inwhich a computer texture overlay is used to describe regions of interestaccording to an embodiment of the invention.

FIG. 16A is a plot depicting the backscattering vs. attenuation datashown in FIG. 14 according to an embodiment of the invention.

FIGS. 16B-16C are plots depicting edge sharpness and texture,respectively, as measured for a boundary of interest according to anembodiment of the invention.

DETAILED DESCRIPTION

The following description refers to the accompanying drawings thatillustrate certain embodiments of the invention. Other embodiments arepossible and modifications may be made to the embodiments withoutdeparting from the spirit and scope of the invention. Therefore, thefollowing detailed description is not meant to limit the invention.Rather, the scope of the invention is defined by the appended claims.

In general, the invention relates to methods for tissue characterizationof vessel walls using optical methods based on what is generally termedlow coherence interferometry (LCI), such as, but not limited to opticalcoherence tomography (OCT) whether in the time or Fourier domain. Themethods described herein solve the problems encountered insemi-automatic or automatic tissue characterization application such asoptical calibration, artifact removal, generating accurate optical andspatial parameter measurement from regions of interests, tissuesegmentations, and statistical discriminant analysis of tissue types. Asused herein discriminant analysis refers to classifying images or datainto different classes.

The present invention provides methods for analyzing OCT data and imagesto characterize biological tissues. Although methods described hereinmay pertain specifically to vascular tissues, the methods also apply totissues in other organs of the body, including tissues in thegastrointestinal, pulmonary, and reproductive tracts. Embodiments of thepresent invention operate in conjunction with an OCT system and acomputing device that include characterization software and a decisiondatabase as discussed below with respect to FIG. 1. Specifically, theOCT console and OCT probe are used to acquire raw OCT data anddemodulated data from a blood vessel. In this context, demodulated datarefers to OCT images, such as grayscale images, or the underlying dataassociate with such images. The OCT data is received by the computingdevice such as a processor and used to create an OCT image on whichnumerical, text or graphical information about tissue characteristicsare displayed.

In part, embodiments of the invention are used to evaluate the walls ofcertain lumens and tissues accessible by an OCT probe. Exemplary tissueimages or tissue data sets can include, but are not limited to plaques,lipid pools, and stent placement zones. Typically, histology images of asample are used to automate an OCT data-based characterization of thesame and unrelated samples. In one embodiment of the present invention,reference data (e.g., normal tissue type data from histology reviewedsamples) are characterized or generated using software comparisons withdata stored in a database. In one embodiment, the characterizationsoftware implements some or all of the steps shown in FIG. 12A. However,this software can implement other methods as appropriate and discussedherein.

In general, one embodiment of the characterization software is based onmanual selection of regions-of-interest in a histology image. The actualhistology data is evaluated to identify different tissue types. In turn,these identified tissue evaluations are be compared to OCT imagesobtained with respect to the same sample tissue. By comparing themanually identified tissues and structures of interest in the histologyimages, training sets are created to allow some of the software andprogramming logic described herein to automatically characterize tissuetypes and structures in an OCT image. For example, if tissue layer A isidentified in the histology image, the same region of interest A′ can beidentified in the corresponding OCT image. This process can be repeatedto build a database of information used to locate different tissues inan OCT image. Backscattering and attenuation data can be used asoutlined below to facilitate this process. Other embodiments of themethods described herein also include image preprocessing steps (such asfocus correction), and optical property measurement.

As part of an exemplary sample measurement session, one or more tissuesamples are first interrogated using OCT such that OCT scan data iscollected. Once the scan data is processed, the resulting OCT imagesrelating to the tissue samples are calibrated and corrected for imagingartifacts. Next, the tissue sample is cross-sectioned to create ahistology image designating different parts of the image as composed ofdifferent elements or features. The tissue samples are processed using ahistological method (such as dye staining), and digitized to create ahistology image or histology data set.

In one embodiment, the OCT images are image mappings of thebackscattered signal that reaches the OCT probe after being reflectedfrom the OCT scan of the sample. In one embodiment, the histology imagesare digitized microscopic images of real tissue sample undergone dyestaining, i.e., the histology images are color images showing the dyedistribution. Since the dyes bind to certain molecules and tissue typespreferentially, the histology images map the molecules/tissue types in atissue sample. As used herein, a histology image typically includes dataregarding tissue or a tissue structure or the image created from suchunderlying data.

The histology image allows operators to identify tissue types (orcharacterizations) and regions of interest (ROIs). The OCT images arematched or mapped to the histology image. In one embodiment, the mappingis done manually. Next, the characterization software then identifies acorresponding region on the OCT image. The characterization softwarethen calculates at least one of the tissue optical properties or spatialfeatures, the result of which is stored in the database. Statisticalanalysis is then applied to form a discriminant analysis method usingboth the OCT data and the tissue types identified in histology.

FIG. 1 is a schematic illustration of a generalized view of an OCTimaging system 10. The imaging system typically includes aninterferometer 12 and an optical source 14, for example, a broadbandlight source, or a wavelength-swept source that provides optical beam toboth sample arm 16 and reference arm 18. The sample arm 16 delivers theoptical beam to tissue through an optional scanning apparatus such as anOCT probe. In one embodiment, the optical scanning apparatus is arotational transducer attached to the end of the sample arm and iscarefully maneuvered through the patient's body to the region ofinterest. The scanning probe provides a substantially collimated beam tothe vessel walls. The reference arm has a built-in known reflector,which may be located either at a separate optical path or at a commonpath as sample arm but at a slightly different location. Thebackscattered light signals from both the sample arm and the referencearm are recombined at the optical interferometer 12.

The combined optical interference signal is converted to electricalsignal by the optical detector (or detector arrays) 20. The signal isthen used to construct images. The detector is in electricalcommunication with a processing and analysis subsystem 21 in oneembodiment. The subsystem can include a processor 22, a data acquisitionmodule 24, and an analysis software application 26.

The processor 22 is a portion of a computer system or otherprocessor-based device executes various software programs or programlogic such as data acquisition 24 and data analysis modules 26. In otherembodiments the acquisition and analysis system elements are hardwaremodules. In one embodiment, the software includes characterizationsoftware and graphic user interface for displaying regions of interestas described below. Typically, the processor 22 is in communication withmemory (not shown) and a database 28. The database is used to store alltypes of data and participate in various processing phases and stages asoutlined below.

OCT is currently the most widespread variant of this group of imagingsystems. The sample arm of commercially available OCT system has manyconfigurations, including microscope, forward-looking endoscope andside-looking endoscope. To simplify description without loss ofgeneralization, OCT with side-looking endoscope is used as anon-limiting example for describing this invention below. In thisconfiguration, an optical probe is mounted in an endoscopic catheter atthe sample arm 16. The substantially collimated beam exits from theoptical probe mounted on the side. Typically the catheter and the probeare rotated to generate 2D scan and can also be pulled or advanced whilerotating to generate 3D scan.

The OCT signal can be described as the light collected from a discretelight imaging volume. Signals from discrete locations within the sampleinclude the image data set. The signal detected from any location isdetermined by the scattering element's size, refractive indices,density, geometrical arrangement, in addition to characteristics of theoptical imaging system. Since different tissues have different chemicalcomposition and microscopic structure, their appearances differ in OCTimages. Qualitative differences in appearance have been used clinicallyfor identifying and characterizing plaques. However, this qualitativeapproach requires extensive experience and is prone to instrumental andhuman errors. To assist in tissue characterization, the presentinvention provides various means to incorporate quantitativemeasurements. In some embodiments, this tissue characterization isperformed automatically using the processor 22 and characterizationsoftware.

In one embodiment of the invention, the characterization software inputsthe OCT data, calibrates the signal strength, enhances data quality viafiltering, corrects imaging artifacts, and calculates parameters for alltissue regions or specific regions of interest identified by operators,and uses the parameters stored in database to identify tissue type orcharacterization. In this embodiment, the characterization softwareidentifies at least one of the tissue optical properties or spatialfeatures from OCT data. The tissue optical properties are calculated anddisplayed. The individual tissue optical properties are displayed eitherindividually or in combination.

To calculate the optical parameters of the tissue, many optical modelscan be used. In one embodiment of the present invention, the opticalparameters of the tissue can be extracted by fitting the data based onsingle-scatterer theory. In another embodiment of the present invention,the optical parameters can be extracted by models, such as the extendedHuygens-Fresnel (ELF) theory that include multiple-scatterer effect.

In one the embodiment, discussed below, P(z) is the power of OCT signalreceived. According to single-scattering theory, the OCT signal powerP(z) collected from a homogeneous sample, from depth z₀ to z₁ isdescribed by:

P(z)=KA(z,φ)T(z ₀)μ_(b)exp(−2μ_(a) z), z₀<z<z₁

log [P(z)]=log [KA(z,φ)T(z ₀)]+log(μ_(b))−2μ_(a) z, z₀<z<z₁  (1.1)

where z is the depth into the sample, K is the delivered incident power,A(z, φ) is the optical system efficiency, T(z₀) is the opticaltransmission efficiency from tissue surface to depth z₀, μ_(b) is thetissue backscattering coefficient, and μ_(a) is the tissue attenuationcoefficient. In A(z, φ), the angular dependence φ arises from varyingbeam delivering efficiency caused by catheter rotation and bloodattenuation. The z dependence is caused by factors such as the divergentbeam focusing profile. The tissue back-scattering coefficients and theattenuation coefficients are characteristic of tissue types and are theprincipal optical parameters used in certain embodiments to determinethe tissue characteristics. For a specific imaging setting, the K valueand A(z, φ) value are constant. For a specific region of interest, theT(z₀) is also a constant. However, there can be variations from theseconstants in some embodiments. Given a substantially constant behaviorfor the different parameters discussed above, a linear relationshipbetween log [P(z)] and the depth z is a reasonable assumption.Accordingly, a line can be fit to the data describing the relationshipbetween scan depth and the signal received by the OCT system. Thislinear model has various uses. For example, based on this linear model,the attenuation coefficient can be calculated from the slope of thefitted line; while the backscattering coefficient can be calculated fromthe offset of the fitted line.

With respect to the depth parameter, the z dependence is illustrated byFIGS. 3A and 3B as discussed below. In turn, the z-dependence can beresolved using a model fit to the data shown in FIG. 3C. The φdependence has many influencing factors that can be addressed usingcertain techniques. In one embodiment, the φ dependence is resolvedusing a model fit to the data of FIG. 4B as described below.

The OCT imaging machine and sample arm beam delivery device has variousoptical efficiencies. To ensure the accurate measurement of tissueoptical properties, in addition to noise subtraction and filtering, theimaging system must be carefully calibrated and various artifacts mustbe removed. As shown in FIG. 2, to calibrate the light intensity exitingthe sample probe, one embodiment of this calibration method places asemi-transparent layer of materials 30, having a known backscatteringcoefficient, around the optical probe 32. In the figure shown, both aredisposed in a lumen of interest 34.

The OCT intensity from this layer 30 is proportional to light intensityexiting the probe, thereby providing a calibrated optical reference.This semi-transparent layer 30 can be in the form of outer sheath of thecatheter, a layer between the outmost sheath and the optical fiberprobe, or a specific semi-transparent coating on the optical fiber, thecatheter sheath or other structural layers in between that hascalibrated reflection coefficients. In order to consider this embodimentin more detail, it is useful to review the components of an exemplaryOCT probe.

In one embodiment, the OCT probe is composed of the rotating opticalfiber 32 surrounded by one or more layers of plastic or glass. Theseconstitute a substantially stationary protective sheath. The partialreflector can be either layer 30 (which is a part of the sheath), or aninterface inside the optical fiber or GRIN lens assembly. The advantageof using layer 30 is that the intensity of layer 30 is generally visiblein the OCT images. The disadvantage of layer 30 is that it is a largerand more complex structure. This greater size and complexity may benon-uniform and the overall layer may have a rotational dependence. Oneadvantage of using the interface as the reflector is that it rotatestogether with the fiber. Hence, it does not suffer potential rotationaldependence. The disadvantage of the interface is that it may lie outsidethe normal OCT scan range (i.e. proximal to the fiber tip, where thenormal OCT image range begins just distal to the fiber tip) requiringthe OCT scan range to adjusted inward to capture this interface andlosing a commensurate portion of the outer scan region.

The partial reflector 30 is used to calibrate the delivered incidentlight intensity (K). Partial reflecting layer 30 can be calibrated byinjecting a laser of known intensity and recording the reflected signalstrength.

An electromagnetic beam such as an opti_(c)al beam suitable forperforming OCT scans is shown in FIG. 3A, where w₀ is the beam waist andz₀ is the Rayleigh range. Due to finite wavelength, the optical beamused in the imaging is a Gaussian beam which is divergent from the beamwaist. It produces a beam focusing profile that can be described by theLorentzian function.

$\begin{matrix}{{A(z)} \propto \frac{z_{0}^{2}}{z_{0}^{2} + \left( {z - f} \right)^{2}}} & (1.2)\end{matrix}$

In equation 1.2, A(z) is light intensity at depth z, z_(o) is theRayleigh range, and f is the focal length of the lens assembly. In ahomogeneous media, the divergent beam profile produces an OCT intensitypattern that peaks at the focal plane, and rolls off from either side,as shown in FIG. 3B. To correct for the loss of intensity due todefocus, the focal length of the imaging probe is measured and theinverse of equation (1.2) is applied to OCT signal P(z). To suppressexcessive boosting of noise in regions far away from the focus plane,the amplification factor is limited far from the focusing point as shownin FIG. 3C.

As shown in FIG. 4A, a probe 50 suitable for collecting OCT data isshown in the cross-section. During in vivo OCT imaging, the light beamtravels through many catheter sheath layers and liquid layers (such assaline or flushing agent) before arriving at the vessel walls. Theselayers may introduce a certain amount of attenuation that cannot be wellpredicted. If the light impinges onto the tissue at an oblique angle,part of its intensity may be lost in the surface reflection. As shown inthe figure, a lumen boundary 53 is shown relative to a superficial layer55. The superficial layer 55 includes tissue a few coherence lengthsaway from the lumen boundary in the depth dimension is segmented. Theregion between the lumen boundary (the inner solid line) and the dottedline is the “superficial layer”. φ is the rotational angle of the probe.

To correct for these effects, if the superficial layer 55 and otherbiological samples are composed of a homogeneous layer of fibrous tissuebeneath the lumen boundary, it is reasonable to use the superficiallayer 55 as a calibration basis for the φ dependence in A(z,φ). To dothis, the boundary 53 between the lumen and the vessel is found eitherby manual selection or by an automatic program. The OCT intensity in theregion is then averaged over the depth to give the angular-dependentintensity profile shown in FIG. 4B. In addition, FIG. 4B shows theangular-dependent intensity profile as a function of φ obtained from thesuperficial layer.

The inverse of this profile shown in FIG. 4B is applied to OCT signal.The application of the inverse profile corrects the rotation-dependentintensity variation and non-uniform blood attenuation in the lumen. Forexample, applying such an inverse profile to an OCT image would yieldthe image shown in FIG. 4A. In general various profiles and models areused to improve OCT image quality as described herein. Another problemwith tissue characterization arises due to unwanted noise which makes itmore challenging to distinguish tissue boundaries and distinguish otherregions of interest.

OCT image noise has several components: shot noise, laser noise andelectrical noise. OCT images are also degraded by speckles. The speckleeffect is inherent to coherent imaging and can reduce the accuracy ofmeasurements of optical properties. To maintain high-resolutionaccuracy, denoising procedures that remove noise without degradingspatial resolution are performed. FIG. 5A shows a “before” image whileFIG. 5B shows an improved “after” image following the application of thedenoising procedures. Suitable denoising techniques and algorithms areknown to those skilled in the art.

As shown in FIG. 6A, in one embodiment of this invention, a window W ofimage data of an appropriate size is taken. This window can be moved asshown in FIG. 6A to select different regions of interest over time (seeFIG. 6B, ROI_(A)). Alternatively, the window W can be resized. In oneembodiment, where no human interaction or automatic segmentation methodis required, a window refers to a one, two, or three-dimensional point,region, or volume of a specified size. This window W is sized such thatthe enclosed region offers sufficient amount of data for reliablemodel-fittings while maintaining sufficient spatial resolution. Theaxial lines (points, planes, or other elements) in the window W areaveraged to produce a depth profile (or other profile) for that window.Model fitting is applied to the depth profile to obtain opticalproperties corresponding to that data window. For example, if asingle-scattering model is employed, the depth profile is scaled aslogarithmic as shown in FIG. 6C to facilitate a linear model fit. Then aline fitting is applied to the profile. Based on Equation (1.1) theoffset of the line fitting offers a measurement of the backscattercoefficient, while the slope offers a measurement of the attenuationcoefficient.

Once an initial data set as been collected, the window W is then movedinside the OCT image to obtain optical properties at different locationsin the image (see arrows shown in FIG. 6A showing possible directions ofwindow movement). In another embodiment, region of interest (such asROI_(A) for example) are drawn either by a human operator or by aprocessor-based computer program or other software module. An exemplaryregion of interest ROI_(A) is shown in FIG. 6B. OCT data inside a regionof interest, such as that shown in FIG. 6B, is averaged to produce adepth profile. In turn, the optical properties for the regions ofinterest are obtained by model fitting such as the linear data fittingshown in FIG. 6C.

The wall of certain lumens, such as an artery, is a layered structurethat includes different tissue components. In some embodiments, thelinear model fitting shown in FIG. 6C is only accurate for a singlelumen layer. Accordingly, to obtaining optical properties for regions ofinterest in a multi-layered structure, the lumen surface and the extentof the OCT penetration limit are first found by processing the OCT imageor optical properties obtained from the OCT image. In one embodiment,this is performed by an automated computer program. In FIG. 7, the outerboundary B_(OCT) shows the penetration limit of the OCT system. Incontrast, the inner boundary of the lumen is show by the inner boundaryB_(Lum). A cross-section of the imaging probe P_(cs) is also shown. Asshown, the visibility of the outer boundary B_(OCT) has been enhanced bythe automated computer program.

In addition to analyzing a multi-layered structure and obtaining opticalproperty data, the boundaries between different tissue types are also ofinterest. In one embodiment of this invention, the tissue boundarydetection is obtained by analyzing a single depth scan. An example ofsuch boundary detection can be understood using the illustrative OCTdata plot and linear curve fitting model in FIG. 8. The depth profile isfitted with piece-wise model. As shown, although non-limiting, differentlinear portions (P1-P3) are shown. Once the piece-wise linearization iscomplete, such as shown in FIG. 8, the discontinuities or break-pointsdenote the tissue boundaries, while each line segment denotes one tissuetype. For example, in FIG. 8, P1 represents or corresponds to the fibertissue, P2 represents or corresponds to calcification tissue, and P3represents or corresponds to lipid tissue. The discontinuity between P1and P2 shows a tissue boundary between the fiber tissue and thecalcification tissue.

In another embodiment of this invention, tissue boundary detection isobtained by analyzing 2D or 3D OCT images, either by a human operator oran automatic algorithm. An example of such boundary detection isillustrated in FIG. 9. As shown, a two-dimensional OCT image of a lumenis shown. The lumen L and calcification C boundaries are detected withautomatic edge-detection methods, e.g., edge detection algorithm.

Once tissue boundary detection is complete, corrected optical propertiesare retrieved by computational models. One example of such a modelcompensates for backscattering by the amount of cumulative attenuationdue to any layers between the region being scanned and the imagingcatheter. For the hypothetical OCT image shown in FIG. 10A, first thetissue region Y is segmented by methods outlined above, which hasdifferent optical properties from the surrounding tissue region X. Theremay be up to N scan lines in the OCT images. Without loss of generality,two scan lines S₁ and S₂ can be considered. These two scan linesintersect with the lumen surface, the top boundary of tissue Y, and thebottom boundary of the tissue Y at A₁, B₁, C₁ and A₂, B₂, C₂,respectively. If the length of segment A₁B₁ and A₂B₂ is d₁, and d₂,respectively, the intensity profile of S₁ and S₂ are shown in FIGS. 10Band 10C, respectively. The slope S_(x, i) and S_(Y, i) of the intensityprofile S₁ and S₂ are calculated in their respective regions, wheresubscript i denotes the different scan lines.

To reduce the effect of noise and speckle, the attenuation coefficientsof tissue X and Y are then calculated as the average of the slopes ofall scan lines. The backscattering coefficient of tissue X is calculatedas the offset of the intensity profile between A_(i) and B_(i). However,because the attenuation effect of tissue X, the backscatteringcoefficients of tissue Y can not be calculated simply by the offset ofthe intensity profile between B_(i) and C_(i). Another approach is used.Specifically, the effect on the offset by the attenuation due to thetissue X on top of tissue Y can be compensated using the followingequation:

O′ _(y,i) =O _(y,i) +S _(x,i) d _(i) i=1, 2,

In the equation above, the O_(y,i) is the offset of the line fitting oftissue Y, S_(x,i) is calculated from the line fitting of tissue X, d_(i)is the thickness of tissue X. The O′_(y,i) and O_(y,i) are thecompensated and the original offset of the line fitting at tissue Y,respectively. S_(x,i) is the slope of the line fitting at tissue X andd_(i) is the depth spanned by scan line inside tissue X.

To reduce the effect of noise and speckle, the backscatteringcoefficients of tissue X and Y are then calculated as the average of thecompensated offsets of all scan lines, Although in the above example thehypothetical image has only two tissue layers, the method can beextended to multiple-layers OCT image by compensating the offsets ofbottom layer iteratively from the top.

Another embodiment of this invention relates to the extraction of imagefeatures associated with specific tissue types based on 2D or 3D images.These features are not extracted solely from a depth-dependent scanningline, but rather rely on analysis of the patterns of neighboring scans.One example is differentiating calcium tissue and lipid tissue. In OCT,both tissue types appear to be signal poor while the surrounding fibrousor ground tissues appear to be signal rich. However, the boundarybetween calcium tissue and fibrous tissue is usually sharp, while theboundary between lipid tissue and fibrous tissue in OCT usually appearsdiffusive. The boundary sharpness can be quantified by measuring thederivative of the image brightness (edge acutance). Other quantifiablelocal image features include texture and shape information.

One semi-automatic method for measuring boundary sharpness requires theoperator to roughly preselect an edge line or a small area enclosing theedge line. Edge detection algorithms (such as Canny's edge detector orregion-growing methods) are then used to detect the precise location ofthe edges. The gray-level variance across the edge line yields a measureof the edge acutance. The edge acutance value is calculated byquantifying the inside-to-outside differences between the signals of theplaque and the surrounding tissue.

In computer vision, texture usually refers to patterns of localvariations in brightness. In an OCT image, texture is closely related tothe speckle formation, which is influenced by the density and sizedistribution of scattering elements or structures. In vessel imaging,under similar focusing conditions, the texture is observed to becorrelated to tissue type. For example, large and densely packedmacrophage foam cells form large speckles and exhibit a “solid” texture;while loosely packed proteoglycan molecules with smaller scatteringelements form small speckles and exhibit a “gel” texture. There arenumerous ways to quantify texture information in computer vision,including methods based on intensity statistics (histogram or variance),local pattern analysis (e.g., spatial gray-level co-occurrencematrices), or spectral analysis.

Different atherosclerosis plaques have different geometrical shapes. Forexample, the foam cells usually form ribbon-like features on theshoulders of large lipid pool. In turn, the media appears like annulusaround the vessel, etc. The shape information is currently used inqualitative assessment of OCT images. In computerized shape analysis,compactness, Fourier descriptors, central invariant moment, andchord-length distributions are the most commonly used methods. It shouldbe appreciated that shape information can be either 2D shape, 3D shapeor both.

It should be appreciated that while optical backscattering coefficient,optical attenuation coefficient, image edge sharpness, image texture,image shape are described in detail above as tissue parameters, thepresent invention is not limited to these parameters. Thus, otherparameters (such as optical anisotropic factor) are within the scope ofthis invention. It should also be appreciated that while models andcalculation methods to derive the parameters described above arepossible methods, there are other physical models or calculation methodsthat are within the scope of this invention.

A quantitative measurement of optical tissue and image properties can bedisplayed to an OCT operator to assist in clinical interpretation. Inone embodiment, the tissue properties are displayed individually. Inanother embodiment, multiple tissue properties are displayed togetherusing a combination display method. For example, there are two tissuecross-sections shown in FIG. 11A. The attenuation coefficients andbackscattering coefficients are shown individually using a grayscalemapping. FIG. 11B shows a combination display method where the color mapis devised to combine the backscattering and attenuation measurements.Letter “C”, “F”, and “L” denote the positions of average backscatteringand attenuation coefficients for calcification, fibrous and lipidtissue, respectively. FIG. 11C shows the images combining backscatteringand attenuation measurements in FIG. 11A using the color-map defined inFIG. 11B. Because the figures are published in black-and-white, FIG. 11Dand FIG. 11E are shown by replacing the color-map in Fig. B and C withhatched texture maps. Improved contrast enhancement can be obtainedusing this approach to visualize plaques. It should be noted thatinstead of texture map, color-map or symbol encoded map using truecolors or symbols can also be used and often generates improvedvisualizations.

In the another embodiment of the present invention, the characterizationsoftware analyzes the OCT data and measured tissue optical properties togenerate image segmentations, define tissue boundaries, and identifytissue elements in samples of interest. The tissue parameters arecalculated for each tissue sample or element thereof and compared to theparameters stored in a database. Based on these results, the tissue typeor characterization is assigned to the tissue sample of element thereofaccording to univariate or multivariate discriminant analysis orclassification. In one embodiment, the calculated tissue parameters aredisplayed as numbers or color-coded images (e.g., discrete colors,grayscale, etc.). In another embodiment, the derived tissue types aredisplayed to the user as texts or color-coded images (e.g., discretecolors, grayscale, etc.). These features are described below in moredetail.

Another method of generating and analyzing quantitative measurement oftissue optical and image properties is shown in FIGS. 12A and 12B. Inone embodiment, two major phases can be used. The first phase is ananalysis and database populating phase and the second phase is a tissuecharacterization phase for a patient. These phases can be performedusing the database and software shown in FIG. 1.

As shown in FIG. 12A, in the database populating phase, the vesselsamples are excised and prepared (step 101) and OCT data is collectedfrom a portion of the vessel. The histology data (step 103) and OCT data(step 104) are collected in parallel as shown. In one embodiment, theOCT data is calibrated and the artifacts removed to generate consistentmeasurement. In one embodiment, data-preprocessing steps (step 108) areperformed. The data-preprocessing steps can include, but are not limitedto calibrating the system power, correcting a focusing effect,correcting angular dependence, and de-noising steps described in textabove and in FIGS. 2, 3, 4, 5. The interrogated portion of the vessel issectioned and processed by standard histology processing. Regions ofinterest encompassing specific tissue type (or vessel characterization)are identified by operator or a machine using certain criteria (Step112). The regions of interest are mapped (step 114) to the OCT imagedata or processor-generated OCT image. Optical properties and spatialfeature extraction (step 118) can also be performed. Finally, anyresolved tissue properties can be stored (step 122) in a database forfuture analysis.

Similarly, FIG. 12B shows a method of identifying tissue type in situ.This method includes the step of collecting OCT data (step 126). Oncecollected, this OCT data is used to generate an OCT image (step 130)with respect to the scanned lumen or other sample of interest. In oneembodiment, the OCT image is then subjected to the data-preprocessingsteps (step 134). Optical properties and spatial feature extraction(step 138) can then be performed as outlined above. Tissue typeidentifiers or signatures are generated (step 142) as discussed above.Next, any suitable tissue type identifiers or signatures are stored(step 146) in the relevant database. At this point, various types ofstatistical analysis (step 150) as described herein can be performed toidentify a particular tissue type in the OCT image using the storedtissue type identifiers or signatures.

During database population phase, different tissue types are identifiedand mapped on the histology images. These tissue identifiers orsignatures that are stored can be used in the future to automaticallyidentify tissue elements and types of interest for new OCT scans. Thecorresponding regions are also identified in the OCT data or image. Anexample of such mapped histology images is shown in FIG. 13A wheretissue types (e.g., fibers (“F”), calcification (“Ca”), etc) wereidentified. The corresponding mapped OCT image is shown in 13B. At leastone optical property and other image features are extracted and storedin a tissue property database. The above-described process is repeatedfor each tissue types and each characterization as many times as desiredto increase the accuracy of the quantitative measurement of parameters.

An example of suitable data for use in the database is shown in FIG. 14,where the attenuation and backscattering coefficients for three tissuetypes are plotted. As shown in the plot, the calcification, fiber andlipid tissue forms clusters that are distinguishable by their positionsin attenuation/backscatter space, which is the basis for the tissuecharacterization. In the tissue characterization phase, the vessel to beinterrogated is imaged with OCT. The image is calibrated and theartifacts are removed. Then regions of interest are generated either byoperator input or by automatic segmentation algorithm. The opticalproperties and other image features are extracted from the regions ofinterest. The quantitative measurements are compared to the tissueproperty database generated in the first phase.

There are many statistical methods to compare the tissue properties of aROI to the database and to assign the tissue type. In one embodiment ofthis invention the discriminant analysis method (or classificationanalysis method) is used to identify tissue types based on the tissueproperties. For example, different tissue types have different theoptical backscattering μ_(b) and the attenuation coefficient μ_(a). Forany ROI to be examined, both parameters are measured. Hence, during thedatabase population phase, the (μ_(b), μ_(a)) pairs of different tissuetypes are obtained.

During characterization phase, the (μ_(b), μ_(a)) pair of the tissue ROIis obtained, and the Mahalanobis distance is calculated between those ofnew acquired ROI and those values from the database. From thecalculation, a decision is made to find the best match. For example, asshown in FIG. 15A, the distance is shortest between the ROI to thefibers in the database, and the ROI is characterized as fibrous tissuewith a certain amount of confidence. The characterization results can bedisplayed as color-coded image or displayed using text legendsdescribing the possible tissue characterization. FIG. 15B is an exampleof such characterization, where the overall gray dotted regionrepresents the fibrous region, while the bottom “-” dotted regionrepresents the lipid region. The different color-coded or selectivelymarked tissue regions and the associated coded legends, include but arenot limited to a fiber, lipid pool, fibrofatty, calcium nodule, calciumplate, calcium speckled, thrombus, foam cells, proteoglycan, and others.FIG. 15B and FIG. 11C are related, but different. In FIG. 15B the tissuetypes are pre-classified for the clinicians, while in FIG. 11C, acontrast-enhanced image is displayed, but the decision of tissue type ismade by clinicians when interpreting the image.

It should also be noted that the optical properties and image featuresfor such discriminant or classification analysis are not limited tobackscattering coefficients and attenuation coefficients, but include,although not limited to edge sharpness, texture parameters, plaquegeometrical shape etc. In addition, the algorithms for performing suchanalysis are not limited to Mahalanobis distance analysis, but include avariety of statistical methods. Biological tissues are complex. Thereare many tissue types and sub-types that possibly could not bedistinguished by only combining the backscattering and attenuationmeasurements. For example, the foam cell tissue, and the lipid both havehigh backscattering and attenuation. Calcification and certain looseconnective tissue both have low backscattering and low attenuation. Inaddition, there are often some overlaps for backscattering andattenuation measurements between different tissue types. For example,some large calcified plaques have small lipid or fibrous tissue pocketsembedded inside, hence having higher backscattering coefficients. Inthese cases, it is often necessary to make additional optical or imageparameters to assist or refine tissue characterization.

Additional parameters may be used for assisting and refining tissuecharacterization. In FIG. 16A is a plot summarizing the backscatteringvs. attenuation plot shown in FIG. 14. There are significant overlapsbetween calcification to the lipid/foam cells tissue. The lipid tissueand foam cell tissue also have similar properties. Therefore, adiscriminant method based on this plot alone is not sufficient todistinguish these tissue types with high accuracy.

FIG. 16B shows a plot of edge sharpness measured for the boundary formedby the calcification tissue and fibrous tissue, and the boundary formedby the calcification tissue and lipid tissue. Significant difference wasfound for the edge transition width between these two types ofboundaries. Hence, FIG. 16B could be used to further refine the tissueclassification.

In OCT coronary artery imaging, foam cells are also an importantindicator of disease state. The foam cells are usually enlargedmacrophage or smooth muscle cells that are filled with lipid droplets.Because of the presence of these lipid droplets, it is often difficultto distinguish them from some lipid tissues. However, because foam cellsare large cells and are often clustered into groups of various size,they tend have different texture appearance from lipid tissues, whichare usually composed of extracellular lipid.

FIG. 16C shows a texture parameter, gray-level co-occurrence matrix forlipid and macrophage tissue. The gray-level co-occurrence matrixcalculation is available in many standard commercial image processingsoftware, such as Matlab (MathWorks, Natick, Mass.). Although there arestill some overlap between the lipid and foam cells in terms of texturemeasurements, the additional information helps to improve tissuecharacterization accuracy.

The above analysis is to analyze edge sharpness and texture measurementsafter analyzing backscattering and attenuation. In other embodiments allof the analysis and comparison can be performed in parallel or in acombination serial/parallel steps. The data and decision shown in FIG.16 is an example and additional parameters and threshold can be usedwith an OCT system trained using histology data as discussed above toidentify any suitable tissue of interest.

Non-Limiting Software Features and Embodiments for Implementing OCTMethods and Systems

The present invention may be embodied in may different forms, including,but in no way limited to, computer program logic for use with aprocessor (e.g., a microprocessor, microcontroller, digital signalprocessor, or general purpose computer), programmable logic for use witha programmable logic device, (e.g., a Field Programmable Gate Array(FPGA) or other PLD), discrete components, integrated circuitry (e.g.,an Application Specific Integrated Circuit (ASIC)), or any other meansincluding any combination thereof. In a typical embodiment of thepresent invention, some or all of the processing of the data collectedusing an OCT probe and the processor-based system is implemented as aset of computer program instructions that is converted into a computerexecutable form, stored as such in a computer readable medium, andexecuted by a microprocessor under the control of an operating system.Thus, query response and input data are transformed into processorunderstandable instructions suitable for generating OCT data, histologyimages, OCT images, ROIs, overlays, signal processing, artifact removal,and other features and embodiments described above.

Computer program logic implementing all or part of the functionalitypreviously described herein may be embodied in various forms, including,but in no way limited to, a source code form, a computer executableform, and various intermediate forms (e.g., forms generated by anassembler, compiler, linker, or locator). Source code may include aseries of computer program instructions implemented in any of variousprogramming languages (e.g., an object code, an assembly language, or ahigh-level language such as Fortran, C, C++, JAVA, or HTML) for use withvarious operating systems or operating environments. The source code maydefine and use various data structures and communication messages. Thesource code may be in a computer executable form (e.g., via aninterpreter), or the source code may be converted (e.g., via atranslator, assembler, or compiler) into a computer executable form.

The computer program may be fixed in any form (e.g., source code form,computer executable form, or an intermediate form) either permanently ortransitorily in a tangible storage medium, such as a semiconductormemory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-ProgrammableRAM), a magnetic memory device (e.g., a diskette or fixed disk), anoptical memory device (e.g., a CD-ROM), a PC card (e.g., PCMCIA card),or other memory device. The computer program may be fixed in any form ina signal that is transmittable to a computer using any of variouscommunication technologies, including, but in no way limited to, analogtechnologies, digital technologies, optical technologies, wirelesstechnologies (e.g., Bluetooth), networking technologies, andinternetworking technologies. The computer program may be distributed inany form as a removable storage medium with accompanying printed orelectronic documentation (e.g., shrink-wrapped software), preloaded witha computer system (e.g., on system ROM or fixed disk), or distributedfrom a server or electronic bulletin board over the communication system(e.g., the Internet or World Wide Web).

Hardware logic (including programmable logic for use with a programmablelogic device) implementing all or part of the functionality previouslydescribed herein may be designed using traditional manual methods, ormay be designed, captured, simulated, or documented electronically usingvarious tools, such as Computer Aided Design (CAD), a hardwaredescription language (e.g., VHDL or AHDL), or a PLD programming language(e.g., PALASM, ABEL, or CUPL).

Programmable logic may be fixed either permanently or transitorily in atangible storage medium, such as a semiconductor memory device (e.g., aRAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memorydevice (e.g., a diskette or fixed disk), an optical memory device (e.g.,a CD-ROM), or other memory device. The programmable logic may be fixedin a signal that is transmittable to a computer using any of variouscommunication technologies, including, but in no way limited to, analogtechnologies, digital technologies, optical technologies, wirelesstechnologies (e.g., Bluetooth), networking technologies, andinternetworking technologies. The programmable logic may be distributedas a removable storage medium with accompanying printed or electronicdocumentation (e.g., shrink-wrapped software), preloaded with a computersystem (e.g., on system ROM or fixed disk), or distributed from a serveror electronic bulletin board over the communication system (e.g., theInternet or World Wide Web).

Various examples of suitable processing modules are discussed below inmore detail. As used herein a module refers to software, hardware, orfirmware suitable for performing a specific data processing or datatransmission task. Typically, in a preferred embodiment a module refersto a software routine, program, or other memory resident applicationsuitable for receiving, transforming, routing and processinginstructions, or various types of data such as OCT scan data,interferometer signal data, clock signals, region of interest types,formulas, and other information of interest.

Computers and computer systems described herein may include operativelyassociated computer-readable media such as memory for storing softwareapplications used in obtaining, processing, storing and/or communicatingdata. It can be appreciated that such memory can be internal, external,remote or local with respect to its operatively associated computer orcomputer system.

Memory may also include any means for storing software or otherinstructions including, for example and without limitation, a hard disk,an optical disk, floppy disk, DVD (digital versatile disc), CD (compactdisc), memory stick, flash memory, ROM (read only memory), RAM (randomaccess memory), DRAM (dynamic random access memory), PROM (programmableROM), EEPROM (extended erasable PROM), and/or other likecomputer-readable media.

In general, computer-readable memory media applied in association withembodiments of the invention described herein may include any memorymedium capable of storing instructions executed by a programmableapparatus. Where applicable, method steps described herein may beembodied or executed as instructions stored on a computer-readablememory medium or memory media. These instructions may be softwareembodied in various programming languages such as C++, C, Java, and/or avariety of other kinds of software programming languages that may beapplied to create instructions in accordance with embodiments of theinvention.

It is to be understood that the figures and descriptions of theinvention have been simplified to illustrate elements that are relevantfor a clear understanding of the invention, while eliminating, forpurposes of clarity, other elements. Those of ordinary skill in the artwill recognize, however, that these and other elements may be desirable.However, because such elements are well known in the art, and becausethey do not facilitate a better understanding of the invention, adiscussion of such elements is not provided herein. It should beappreciated that the figures are presented for illustrative purposes andnot as construction drawings. Omitted details and modifications oralternative embodiments are within the purview of persons of ordinaryskill in the art.

It can be appreciated that, in certain aspects of the invention, asingle component may be replaced by multiple components, and multiplecomponents may be replaced by a single component, to provide an elementor structure or to perform a given function or functions. Except wheresuch substitution would not be operative to practice certain embodimentsof the invention, such substitution is considered within the scope ofthe invention.

The examples presented herein are intended to illustrate potential andspecific implementations of the invention. It can be appreciated thatthe examples are intended primarily for purposes of illustration of theinvention for those skilled in the art. There may be variations to thesediagrams or the operations described herein without departing from thespirit of the invention. For instance, in certain cases, method steps oroperations may be performed or executed in differing order, oroperations may be added, deleted or modified.

Furthermore, whereas particular embodiments of the invention have beendescribed herein for the purpose of illustrating the invention and notfor the purpose of limiting the same, it will be appreciated by those ofordinary skill in the art that numerous variations of the details,materials and arrangement of elements, steps, structures, and/or partsmay be made within the principle and scope of the invention withoutdeparting from the invention as described in the claims.

1. A processor-implemented method for identifying tissue components insitu comprising the steps of: a. collecting an OCT dataset of a tissuesample in situ using a probe; b. measuring an attenuation value and abackscattering value at a point in the tissue sample; and c. determininga tissue characteristic at a location in the tissue sample correspondingto an image location in an OCT image formed from the OCT dataset inresponse to the measured attenuation value and backscattering value. 2.The method of claim 1 further comprising mapping a pair of coordinatesin backscatter-attenuation space to an indicium of the value of the pairof coordinates in the backscatter-attenuation space.
 3. The method ofclaim 2 wherein the indicium is a color.
 4. The method of claim 2further comprising displaying the indicium corresponding to the measuredattenuation and backscatter at the point in the OCT image.
 5. The methodof claim 1 wherein the tissue characteristic is selected from the groupconsisting of cholesterol, fiber, lipid pool, fibrofatty, calcification,red thrombus, white thrombus, foam cells, and proteoglycan.
 6. Themethod of claim 1 wherein the indicium is selected from the groupconsisting of an over-lay, a colormap, a texture map, and text.
 7. Themethod of claim 1 further comprising the step of classifying tissue typeusing a property selected from the group consisting of backscattering,attenuation, edge sharpness and texture measurements.
 8. The method ofclaim 1 further comprising the step of correcting a focusing effect toimprove tissue type classification.
 9. The method of claim 1 furthercomprising the step of applying angular intensity correction to accountfor an attenuation effect.
 10. The method of claim 9 wherein theattenuation effect is blood related.
 11. The method of claim 1 furthercomprising the step of determining a tissue characteristic using atechnique selected from the group consisting of boundary detection,lumen location, and OCT location depth determination.
 12. A system foridentifying tissue components in situ comprising: a. an OCT subsystemfor taking an OCT image of a tissue in situ; b. a processor incommunication with the OCT subsystem for measuring the attenuation andbackscatter at a point in the OCT image and determining a tissuecharacteristic of the tissue at a location in the tissue correspondingto the point in the OCT image in response to the measured attenuationand backscatter; and c. a display for displaying the OCT image and anindicium corresponding to the measured attenuation and backscatter atthe point in the OCT image.
 13. The system of claim 12 wherein thetissue characteristic is selected from the group consisting ofcholesterol, fiber, fibrous, lipid pool, lipid, fibrofatty, calciumnodule, calcium plate, calcium speckled, thrombus, foam cells, andproteoglycan.
 14. An optical coherence tomography system for identifyingtissue characteristics of a sample, the computer system comprising: adetector configured to receive an optical interference signal generatedfrom scanning a sample and converting the optical interference signal toan electrical signal; an electronic memory device and an electronicprocessor in communication with the memory device and the detector,wherein the memory device comprises instructions that when executed bythe processor cause the processor to: analyze the electrical signal andgenerate a plurality of datasets corresponding to the sample, whereinone of the plurality of datasets comprises backscattering data; comparethe backscattering data to a first threshold, the backscattering datamapping to a first location in the sample; and if the backscatteringdata exceeds the first threshold, characterize the first location in thesample as having a first tissue characteristic.
 15. The system of claim14 wherein the first tissue characteristic is selected from the groupconsisting of cholesterol, fiber, fibrous, lipid pool, lipid,fibrofatty, calcium nodule, calcium plate, calcium speckled, thrombus,foam cells, and proteoglycan.
 16. The system of claim 14 wherein theprocessor is further caused to generate an OCT image of the sample suchthat the first tissue characteristic is identified and displayedrelative to the first location.
 17. The system of claim 14 wherein oneof the plurality of datasets comprises OCT scan data, attenuation data,edge sharpness data, texture parameters, and interferometric data.